Median-mean line based discriminant analysis

Neurocomputing

Volumn 123, Issue , 2014, Pages 233-246

  Xu, Jie ^a,b   Yang, Jian ^b,c   Gu, Zhenghong ^b   Zhang, Nan ^b  

^a Guangzhou Jiaotong University   (China)

^b NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Dimensionality reduction; Fisher linear discriminant analysis (FLDA); Median mean line; Pattern recognition

Indexed keywords

DIMENSIONALITY REDUCTION; FACE DATABASE; FISHER LINEAR DISCRIMINANT ANALYSIS; MEDIAN-MEAN LINE; OBJECT CATEGORIES; WITHIN CLASS; YALE FACE DATABASE;

DISCRIMINANT ANALYSIS; PATTERN RECOGNITION;

DATABASE SYSTEMS;

ARTICLE; DATA BASE; DISCRIMINANT ANALYSIS; FISHER LINEAR DISCRIMINANT ANALYSIS; INTERMETHOD COMPARISON; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; MEDIAN MEAN LINE BASED DISCRIMINANT ANALYSIS; NEUROSCIENCE; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

EID: 84885844113     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.07.012     Document Type: Article

References (33)

1. Jain A.K., Duin R.P.W., Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(1):4-37.
2. Jolliffe I. Principal Component Analysis 2002, Springer.
3. Belhumeur P.N., Hespanha J.P., Kriegman D.J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19:711-720.
4. Turk M., Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience 1991, 3:71-86.
5. Fisher R.A. The statistical utilization of multiple measurements. Annals of Eugenics 1938, 8:376-386.
6. Swets D.L., Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996, 18(8):831-836.
7. Fukunaga K. Introduction to Statistical Pattern Recognition 1990, Academic Press, New York.
8. Chen L., Liao H., Ko M., Lin J., Yu G. A new LDA-based face recognition system, which can solve the small sample size problem. Pattern Recognition 2000, 33(10):1713-1726.
9. Wang X., Tang X. Dual-space linear discriminant analysis for face recognition. Proceedings of Computer Vision and Pattern Recognition (CVPR 2004) 2004, 2:564-569.
10. Cevikalp H., Neamtu M., Wilkes M., Barkana A. Discriminative common vectors for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(1):4-13.
11. R. Huang, Q. Liu, H. Lu, S. Ma, Solving the small sample size problem in LDA, in: ICPR, 2002.
12. Lu J., Plataniotis K.N., Venetsanopoulos A.N. Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recognition Letters 2005, 26(2):181-191.
13. Hong Z.Q., Yang J.Y. Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition 1991, 24:317-324.
14. Dai D.Q., Yuen P.C. Regularized discriminant analysis and its application to face recognition. Pattern Recognition 2003, 36:845-847.
15. P. Zhang, J. Peng, N. Riedel, Discriminant analysis: a least squares approximation view, in: CVPR, 2005.
16. W. Zhao, R. Chellappa, P.J. Phillips, Subspace Linear Discriminant Analysis for Face Recognition. Technical Report CAR-TR-914, CS-TR-4009, University of Maryland at College Park, USA, 1999.
17. Yang J., Yang J.Y. Why can LDA be performed in PCA transformed space?. Pattern Recognition 2003, 36(2):563-566.
18. Yu H., Yang J. A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition 2001, 34:2067-2070.
19. Li M., Yuan B. 2D-LDA: a novel statistical linear discriminant analysis for image matrix. Pattern Recognition Letters 2005, 26(55):527-532.
20. Xiong H., Swamy M.N.S., Ahmad M.O., Two-dimensional FLD for face recognition. Pattern Recognition 2005, 38(7):1121-1124.
21. J. Yang, J.Y. Yang, Optimal FLD algorithm for facial feature extraction, in: SPIE Proceedings of the Intelligent Robots and Computer Vision XX: Algorithm, Techniques, and Active Vision, vol. 4572, pp. 438-4442001.
22. J. Ye, R. Janardan, Q. Li, Two-dimensional linear discriminant analysis, in: NIPS, 2004.
23. Nie F.P., Xing S.M., Song Y.Q., Zhang C.S. Extracting the optimal dimensionality analysis. Pattern Recognition 2009, 42:105-114.
24. Gonzalez R.C., Woods R.E. Digital Image Processing 1997, Addison Wesley.
25. Yang J., Zhang D., Yang J.Y., Niu B. Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29(4):650-664.
26. Yang J., Zhang L., Yang J.Y., Zhang D. From classifiers to discriminators: a nearest neighbor rule induced discriminant analysis. Pattern Recognition 2011, 44(7):1387-1402.
27. X. He, P. Niyogi, Locality preserving projections, in: Proceedings of the 16th Conference on Neural Information Processing Systems, 2003.
28. He X., Yan S., Hu Y., Niyogi P., Zhang H.J. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(3):328-340.
29. A.M. Martinez, R. Benavente, The AR Face Database, 2006 http://rvl1.ecn.purdue.edu/aleix/~aleix_face_DB.html.
30. A.M. Martinez, R. Benavente, The AR Face Database, CVC Technical Report #24, June 1998.
31. Raghavan V., Bollmann P., Jung G.S. A critical investigation of recall and precision as measures of retrieval system performance. ACM Transactions on Information Systems 1989, 7(3):205-229.
32. B. Leibe and B. Schiele, Analyzing appearance and contour based methods for object categorization, in: International Conference on Computer Vision and Pattern Recognition (CVPR'03), Madison, WI, June 2003.
33. B. Leibe, The ETH-80 Image Set, http://www.mis.informatik.tu-darmstadt.de/Research/Projects/categorization/eth80-db.html.